Overview

Dataset statistics

Number of variables16
Number of observations5854
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory190.1 B

Variable types

Text1
Numeric14
Categorical1

Alerts

danceability is highly overall correlated with valenceHigh correlation
energy is highly overall correlated with loudness and 1 other fieldsHigh correlation
valence is highly overall correlated with danceabilityHigh correlation
loudness is highly overall correlated with energy and 2 other fieldsHigh correlation
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
popularity is highly overall correlated with loudnessHigh correlation
artist_id has unique valuesUnique
key has 673 (11.5%) zerosZeros
instrumentalness has 481 (8.2%) zerosZeros

Reproduction

Analysis started2023-11-17 01:04:39.591778
Analysis finished2023-11-17 01:05:12.119367
Duration32.53 seconds
Software versionydata-profiling vv4.6.0
Download configurationconfig.json

Variables

Distinct5810
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size400.9 KiB
2023-11-17T01:05:12.406876image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Length

Max length46
Median length37
Mean length12.129655
Min length1

Characters and Unicode

Total characters71007
Distinct characters101
Distinct categories17 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5769 ?
Unique (%)98.5%

Sample

1st rowFrank Sinatra
2nd rowVladimir Horowitz
3rd rowJohnny Cash
4th rowBillie Holiday
5th rowBob Dylan
ValueCountFrequency (%)
the 716
 
6.0%
136
 
1.1%
of 67
 
0.6%
john 62
 
0.5%
band 42
 
0.4%
johnny 37
 
0.3%
james 37
 
0.3%
joe 36
 
0.3%
paul 35
 
0.3%
david 32
 
0.3%
Other values (6549) 10732
89.9%
2023-11-17T01:05:12.924638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6913
 
9.7%
6078
 
8.6%
a 5302
 
7.5%
n 4315
 
6.1%
r 4218
 
5.9%
o 4113
 
5.8%
i 4049
 
5.7%
l 3213
 
4.5%
s 3035
 
4.3%
t 2839
 
4.0%
Other values (91) 26932
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 52062
73.3%
Uppercase Letter 12067
 
17.0%
Space Separator 6078
 
8.6%
Other Punctuation 474
 
0.7%
Decimal Number 132
 
0.2%
Dash Punctuation 62
 
0.1%
Other Symbol 50
 
0.1%
Other Number 27
 
< 0.1%
Format 21
 
< 0.1%
Currency Symbol 10
 
< 0.1%
Other values (7) 24
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1169
 
9.7%
B 1023
 
8.5%
S 999
 
8.3%
M 907
 
7.5%
C 904
 
7.5%
J 678
 
5.6%
R 615
 
5.1%
D 613
 
5.1%
L 582
 
4.8%
A 570
 
4.7%
Other values (17) 4007
33.2%
Lowercase Letter
ValueCountFrequency (%)
e 6913
13.3%
a 5302
10.2%
n 4315
 
8.3%
r 4218
 
8.1%
o 4113
 
7.9%
i 4049
 
7.8%
l 3213
 
6.2%
s 3035
 
5.8%
t 2839
 
5.5%
h 2307
 
4.4%
Other values (16) 11758
22.6%
Other Punctuation
ValueCountFrequency (%)
. 156
32.9%
& 134
28.3%
' 67
14.1%
" 38
 
8.0%
¡ 19
 
4.0%
, 19
 
4.0%
! 16
 
3.4%
6
 
1.3%
/ 6
 
1.3%
* 5
 
1.1%
Other values (5) 8
 
1.7%
Decimal Number
ValueCountFrequency (%)
0 22
16.7%
1 21
15.9%
2 19
14.4%
5 15
11.4%
3 13
9.8%
4 12
9.1%
7 10
7.6%
9 8
 
6.1%
8 6
 
4.5%
6 6
 
4.5%
Currency Symbol
ValueCountFrequency (%)
£ 5
50.0%
¤ 2
 
20.0%
¢ 2
 
20.0%
$ 1
 
10.0%
Modifier Symbol
ValueCountFrequency (%)
¸ 3
37.5%
´ 2
25.0%
¯ 2
25.0%
˜ 1
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 61
98.4%
1
 
1.6%
Other Number
ValueCountFrequency (%)
³ 23
85.2%
¼ 4
 
14.8%
Math Symbol
ValueCountFrequency (%)
± 4
80.0%
+ 1
 
20.0%
Initial Punctuation
ValueCountFrequency (%)
« 2
66.7%
1
33.3%
Space Separator
ValueCountFrequency (%)
6078
100.0%
Other Symbol
ValueCountFrequency (%)
© 50
100.0%
Format
ValueCountFrequency (%)
­ 21
100.0%
Other Letter
ValueCountFrequency (%)
º 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Control
ValueCountFrequency (%)
 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 64132
90.3%
Common 6875
 
9.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6913
 
10.8%
a 5302
 
8.3%
n 4315
 
6.7%
r 4218
 
6.6%
o 4113
 
6.4%
i 4049
 
6.3%
l 3213
 
5.0%
s 3035
 
4.7%
t 2839
 
4.4%
h 2307
 
3.6%
Other values (44) 23828
37.2%
Common
ValueCountFrequency (%)
6078
88.4%
. 156
 
2.3%
& 134
 
1.9%
' 67
 
1.0%
- 61
 
0.9%
© 50
 
0.7%
" 38
 
0.6%
³ 23
 
0.3%
0 22
 
0.3%
­ 21
 
0.3%
Other values (37) 225
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70695
99.6%
None 307
 
0.4%
Punctuation 4
 
< 0.1%
Modifier Letters 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6913
 
9.8%
6078
 
8.6%
a 5302
 
7.5%
n 4315
 
6.1%
r 4218
 
6.0%
o 4113
 
5.8%
i 4049
 
5.7%
l 3213
 
4.5%
s 3035
 
4.3%
t 2839
 
4.0%
Other values (68) 26620
37.7%
None
ValueCountFrequency (%)
à 156
50.8%
© 50
 
16.3%
³ 23
 
7.5%
­ 21
 
6.8%
¡ 19
 
6.2%
6
 
2.0%
£ 5
 
1.6%
± 4
 
1.3%
¼ 4
 
1.3%
º 3
 
1.0%
Other values (9) 16
 
5.2%
Punctuation
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Modifier Letters
ValueCountFrequency (%)
˜ 1
100.0%

artist_id
Real number (ℝ)

UNIQUE 

Distinct5854
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean588858.8
Minimum74
Maximum3670556
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2023-11-17T01:05:13.100026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum74
5-th percentile31917.9
Q1169491.5
median444068.5
Q3785732.5
95-th percentile2135968.1
Maximum3670556
Range3670482
Interquartile range (IQR)616241

Descriptive statistics

Standard deviation652206.11
Coefficient of variation (CV)1.1075764
Kurtosis7.4723454
Mean588858.8
Median Absolute Deviation (MAD)309027.5
Skewness2.580991
Sum3.4471794 × 109
Variance4.2537281 × 1011
MonotonicityNot monotonic
2023-11-17T01:05:13.254471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
792507 1
 
< 0.1%
320470 1
 
< 0.1%
314050 1
 
< 0.1%
313899 1
 
< 0.1%
303422 1
 
< 0.1%
285693 1
 
< 0.1%
273105 1
 
< 0.1%
261309 1
 
< 0.1%
253232 1
 
< 0.1%
247545 1
 
< 0.1%
Other values (5844) 5844
99.8%
ValueCountFrequency (%)
74 1
< 0.1%
335 1
< 0.1%
441 1
< 0.1%
589 1
< 0.1%
1097 1
< 0.1%
1098 1
< 0.1%
1113 1
< 0.1%
1163 1
< 0.1%
1190 1
< 0.1%
1266 1
< 0.1%
ValueCountFrequency (%)
3670556 1
< 0.1%
3661738 1
< 0.1%
3661296 1
< 0.1%
3659356 1
< 0.1%
3639618 1
< 0.1%
3637248 1
< 0.1%
3632715 1
< 0.1%
3606027 1
< 0.1%
3567510 1
< 0.1%
3559122 1
< 0.1%

danceability
Real number (ℝ)

HIGH CORRELATION 

Distinct4160
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54534133
Minimum0.0804
Maximum0.962
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2023-11-17T01:05:13.406615image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0804
5-th percentile0.31288333
Q10.46024878
median0.55013139
Q30.63695
95-th percentile0.76064808
Maximum0.962
Range0.8816
Interquartile range (IQR)0.17670122

Descriptive statistics

Standard deviation0.13516578
Coefficient of variation (CV)0.24785537
Kurtosis0.072391068
Mean0.54534133
Median Absolute Deviation (MAD)0.088131385
Skewness-0.23041969
Sum3192.4281
Variance0.018269788
MonotonicityNot monotonic
2023-11-17T01:05:13.543128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.478 11
 
0.2%
0.562 10
 
0.2%
0.617 10
 
0.2%
0.582 10
 
0.2%
0.484 10
 
0.2%
0.458 9
 
0.2%
0.475 9
 
0.2%
0.587 9
 
0.2%
0.628 9
 
0.2%
0.547 8
 
0.1%
Other values (4150) 5759
98.4%
ValueCountFrequency (%)
0.0804 1
< 0.1%
0.0811 1
< 0.1%
0.084 2
< 0.1%
0.0866 1
< 0.1%
0.0912 1
< 0.1%
0.096575 1
< 0.1%
0.105 1
< 0.1%
0.109 1
< 0.1%
0.1106 1
< 0.1%
0.114 1
< 0.1%
ValueCountFrequency (%)
0.962 1
< 0.1%
0.928 1
< 0.1%
0.912 1
< 0.1%
0.908 1
< 0.1%
0.9075 1
< 0.1%
0.906 1
< 0.1%
0.903 1
< 0.1%
0.901 1
< 0.1%
0.898190476 1
< 0.1%
0.896 1
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct4484
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58545629
Minimum0.00198
Maximum0.9995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2023-11-17T01:05:13.693719image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.00198
5-th percentile0.2079426
Q10.43
median0.59848344
Q30.75131262
95-th percentile0.92215729
Maximum0.9995
Range0.99752
Interquartile range (IQR)0.32131262

Descriptive statistics

Standard deviation0.21605258
Coefficient of variation (CV)0.36903282
Kurtosis-0.59909873
Mean0.58545629
Median Absolute Deviation (MAD)0.15981677
Skewness-0.26686371
Sum3427.2611
Variance0.046678718
MonotonicityNot monotonic
2023-11-17T01:05:13.835851image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.43 10
 
0.2%
0.933 10
 
0.2%
0.587 9
 
0.2%
0.725 9
 
0.2%
0.872 8
 
0.1%
0.647 8
 
0.1%
0.632 7
 
0.1%
0.658 7
 
0.1%
0.52 7
 
0.1%
0.82 7
 
0.1%
Other values (4474) 5772
98.6%
ValueCountFrequency (%)
0.00198 1
< 0.1%
0.00249 2
< 0.1%
0.00459 1
< 0.1%
0.005 1
< 0.1%
0.00835 2
< 0.1%
0.01305 1
< 0.1%
0.0144 1
< 0.1%
0.0156 1
< 0.1%
0.0175 1
< 0.1%
0.01835 1
< 0.1%
ValueCountFrequency (%)
0.9995 1
 
< 0.1%
0.999 1
 
< 0.1%
0.998 1
 
< 0.1%
0.994666667 1
 
< 0.1%
0.9935 1
 
< 0.1%
0.9925 1
 
< 0.1%
0.992 2
< 0.1%
0.991 3
0.1%
0.99 1
 
< 0.1%
0.989 2
< 0.1%

valence
Real number (ℝ)

HIGH CORRELATION 

Distinct4480
Distinct (%)76.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.54514213
Minimum0.02785
Maximum0.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2023-11-17T01:05:13.989909image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.02785
5-th percentile0.196
Q10.40964463
median0.554
Q30.689
95-th percentile0.871
Maximum0.98
Range0.95215
Interquartile range (IQR)0.27935537

Descriptive statistics

Standard deviation0.20027032
Coefficient of variation (CV)0.36737268
Kurtosis-0.42368669
Mean0.54514213
Median Absolute Deviation (MAD)0.13993788
Skewness-0.18790922
Sum3191.262
Variance0.040108202
MonotonicityNot monotonic
2023-11-17T01:05:14.131783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.649 11
 
0.2%
0.679 8
 
0.1%
0.471 8
 
0.1%
0.667 8
 
0.1%
0.96 7
 
0.1%
0.777 7
 
0.1%
0.755 7
 
0.1%
0.961 7
 
0.1%
0.64 7
 
0.1%
0.601 7
 
0.1%
Other values (4470) 5777
98.7%
ValueCountFrequency (%)
0.02785 1
< 0.1%
0.0279 1
< 0.1%
0.03055 1
< 0.1%
0.032675 1
< 0.1%
0.0328 1
< 0.1%
0.03312 1
< 0.1%
0.03505 1
< 0.1%
0.0352 1
< 0.1%
0.03585 1
< 0.1%
0.0364 1
< 0.1%
ValueCountFrequency (%)
0.98 1
 
< 0.1%
0.979 2
< 0.1%
0.978 1
 
< 0.1%
0.976 1
 
< 0.1%
0.972 2
< 0.1%
0.971 1
 
< 0.1%
0.97 2
< 0.1%
0.96975 1
 
< 0.1%
0.969 2
< 0.1%
0.968 3
0.1%

tempo
Real number (ℝ)

Distinct5769
Distinct (%)98.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.35704
Minimum30.946
Maximum206.68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2023-11-17T01:05:14.275792image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum30.946
5-th percentile90.49925
Q1109.46207
median119.31151
Q3129.84401
95-th percentile154.2951
Maximum206.68
Range175.734
Interquartile range (IQR)20.381941

Descriptive statistics

Standard deviation19.170434
Coefficient of variation (CV)0.15927971
Kurtosis1.7917952
Mean120.35704
Median Absolute Deviation (MAD)10.195527
Skewness0.57041135
Sum704570.1
Variance367.50555
MonotonicityNot monotonic
2023-11-17T01:05:14.411462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120.4341133 3
 
0.1%
141.227 3
 
0.1%
103.7140321 3
 
0.1%
150.238 3
 
0.1%
113.325 3
 
0.1%
193.041 3
 
0.1%
101.3285 2
 
< 0.1%
114.176 2
 
< 0.1%
130.693 2
 
< 0.1%
83.444 2
 
< 0.1%
Other values (5759) 5828
99.6%
ValueCountFrequency (%)
30.946 1
< 0.1%
55.807 1
< 0.1%
56.45466667 1
< 0.1%
58.66 1
< 0.1%
59.592 1
< 0.1%
62.825 1
< 0.1%
64.979 1
< 0.1%
66.668 1
< 0.1%
67.117 2
< 0.1%
67.469 1
< 0.1%
ValueCountFrequency (%)
206.68 1
< 0.1%
205.703 1
< 0.1%
203.179 1
< 0.1%
202.409 1
< 0.1%
202.318 1
< 0.1%
201.885 1
< 0.1%
201.712 1
< 0.1%
200.033 1
< 0.1%
199.693 1
< 0.1%
199.04 1
< 0.1%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct5653
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.9564999
Minimum-40.147
Maximum1.342
Zeros0
Zeros (%)0.0%
Negative5853
Negative (%)> 99.9%
Memory size45.9 KiB
2023-11-17T01:05:14.567387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-40.147
5-th percentile-17.202964
Q1-12.240257
median-9.528125
Q3-6.8652932
95-th percentile-4.35678
Maximum1.342
Range41.489
Interquartile range (IQR)5.3749636

Descriptive statistics

Standard deviation4.1796367
Coefficient of variation (CV)-0.41978976
Kurtosis2.9341953
Mean-9.9564999
Median Absolute Deviation (MAD)2.689375
Skewness-1.10618
Sum-58285.35
Variance17.469363
MonotonicityNot monotonic
2023-11-17T01:05:14.706183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-7.305 3
 
0.1%
-6.944 3
 
0.1%
-5.384 3
 
0.1%
-10.384 3
 
0.1%
-9.344 3
 
0.1%
-17.88647857 3
 
0.1%
-12.22543333 3
 
0.1%
-5.541 3
 
0.1%
-6.979 3
 
0.1%
-20.396 3
 
0.1%
Other values (5643) 5824
99.5%
ValueCountFrequency (%)
-40.147 1
< 0.1%
-35.951 1
< 0.1%
-35.116 1
< 0.1%
-34.366 2
< 0.1%
-33.47625 1
< 0.1%
-33.475 2
< 0.1%
-32.667 1
< 0.1%
-32.652 1
< 0.1%
-32.347 1
< 0.1%
-30.84 1
< 0.1%
ValueCountFrequency (%)
1.342 1
< 0.1%
-0.19 1
< 0.1%
-0.866 1
< 0.1%
-1.092 1
< 0.1%
-1.158 1
< 0.1%
-1.387 1
< 0.1%
-1.714 1
< 0.1%
-1.731 1
< 0.1%
-1.847 1
< 0.1%
-1.987 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size331.7 KiB
1
4808 
0
1046 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5854
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 4808
82.1%
0 1046
 
17.9%

Length

2023-11-17T01:05:14.823759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-17T01:05:14.936393image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 4808
82.1%
0 1046
 
17.9%

Most occurring characters

ValueCountFrequency (%)
1 4808
82.1%
0 1046
 
17.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5854
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4808
82.1%
0 1046
 
17.9%

Most occurring scripts

ValueCountFrequency (%)
Common 5854
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4808
82.1%
0 1046
 
17.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5854
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 4808
82.1%
0 1046
 
17.9%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5133242
Minimum0
Maximum11
Zeros673
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2023-11-17T01:05:15.021450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5183845
Coefficient of variation (CV)0.63816028
Kurtosis-1.2332734
Mean5.5133242
Median Absolute Deviation (MAD)3
Skewness-0.13070669
Sum32275
Variance12.37903
MonotonicityNot monotonic
2023-11-17T01:05:15.113596image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 934
16.0%
9 742
12.7%
0 673
11.5%
2 641
10.9%
5 530
9.1%
11 463
7.9%
1 403
6.9%
4 387
6.6%
10 361
 
6.2%
6 321
 
5.5%
Other values (2) 399
6.8%
ValueCountFrequency (%)
0 673
11.5%
1 403
6.9%
2 641
10.9%
3 129
 
2.2%
4 387
6.6%
5 530
9.1%
6 321
 
5.5%
7 934
16.0%
8 270
 
4.6%
9 742
12.7%
ValueCountFrequency (%)
11 463
7.9%
10 361
 
6.2%
9 742
12.7%
8 270
 
4.6%
7 934
16.0%
6 321
 
5.5%
5 530
9.1%
4 387
6.6%
3 129
 
2.2%
2 641
10.9%

acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct5261
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34356747
Minimum1.01 × 10-6
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2023-11-17T01:05:15.237321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.01 × 10-6
5-th percentile0.0017
Q10.081455
median0.268
Q30.57711111
95-th percentile0.88835
Maximum0.996
Range0.99599899
Interquartile range (IQR)0.49565611

Descriptive statistics

Standard deviation0.29486997
Coefficient of variation (CV)0.85825928
Kurtosis-0.90860929
Mean0.34356747
Median Absolute Deviation (MAD)0.21974735
Skewness0.5918281
Sum2011.244
Variance0.086948302
MonotonicityNot monotonic
2023-11-17T01:05:15.385991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.35 6
 
0.1%
0.988 5
 
0.1%
0.367 5
 
0.1%
0.273 5
 
0.1%
0.299 5
 
0.1%
0.815 5
 
0.1%
0.102 5
 
0.1%
0.125 4
 
0.1%
0.2 4
 
0.1%
0.161 4
 
0.1%
Other values (5251) 5806
99.2%
ValueCountFrequency (%)
1.01 × 10-61
< 0.1%
1.41 × 10-61
< 0.1%
1.59 × 10-61
< 0.1%
3.37 × 10-61
< 0.1%
3.48 × 10-61
< 0.1%
4.12 × 10-61
< 0.1%
4.21 × 10-61
< 0.1%
4.41 × 10-61
< 0.1%
4.45 × 10-61
< 0.1%
6.51 × 10-61
< 0.1%
ValueCountFrequency (%)
0.996 3
0.1%
0.9948125 1
 
< 0.1%
0.994666667 1
 
< 0.1%
0.993 2
< 0.1%
0.992 4
0.1%
0.991333333 1
 
< 0.1%
0.991 2
< 0.1%
0.990070423 1
 
< 0.1%
0.99 2
< 0.1%
0.9895 1
 
< 0.1%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct4931
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13233422
Minimum0
Maximum0.973
Zeros481
Zeros (%)8.2%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2023-11-17T01:05:15.530698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.7 × 10-5
median0.0088016735
Q30.14299193
95-th percentile0.748425
Maximum0.973
Range0.973
Interquartile range (IQR)0.14289493

Descriptive statistics

Standard deviation0.23352957
Coefficient of variation (CV)1.7646953
Kurtosis2.9427987
Mean0.13233422
Median Absolute Deviation (MAD)0.0088016735
Skewness1.9953195
Sum774.6845
Variance0.054536058
MonotonicityNot monotonic
2023-11-17T01:05:15.690233image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 481
 
8.2%
1.18 × 10-57
 
0.1%
2.54 × 10-55
 
0.1%
2.51 × 10-55
 
0.1%
1.02 × 10-55
 
0.1%
0.00225 4
 
0.1%
0.832 4
 
0.1%
1.16 × 10-54
 
0.1%
0.00212 4
 
0.1%
1.3 × 10-54
 
0.1%
Other values (4921) 5331
91.1%
ValueCountFrequency (%)
0 481
8.2%
6.65 × 10-81
 
< 0.1%
7.23 × 10-81
 
< 0.1%
8.21 × 10-81
 
< 0.1%
8.83 × 10-81
 
< 0.1%
1.09 × 10-71
 
< 0.1%
1.28 × 10-71
 
< 0.1%
1.46 × 10-71
 
< 0.1%
1.54 × 10-71
 
< 0.1%
1.69 × 10-71
 
< 0.1%
ValueCountFrequency (%)
0.973 1
 
< 0.1%
0.963 1
 
< 0.1%
0.962 1
 
< 0.1%
0.957 1
 
< 0.1%
0.9525 1
 
< 0.1%
0.951 3
0.1%
0.949 1
 
< 0.1%
0.9436875 1
 
< 0.1%
0.943 2
< 0.1%
0.942 1
 
< 0.1%

liveness
Real number (ℝ)

Distinct4752
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19572715
Minimum0.0116
Maximum0.973
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2023-11-17T01:05:15.833108image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0116
5-th percentile0.0792325
Q10.12493889
median0.16995917
Q30.22936382
95-th percentile0.39226889
Maximum0.973
Range0.9614
Interquartile range (IQR)0.10442493

Descriptive statistics

Standard deviation0.11616136
Coefficient of variation (CV)0.59348617
Kurtosis10.327332
Mean0.19572715
Median Absolute Deviation (MAD)0.049927143
Skewness2.6256233
Sum1145.7867
Variance0.01349346
MonotonicityNot monotonic
2023-11-17T01:05:15.982619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.103 16
 
0.3%
0.102 16
 
0.3%
0.109 15
 
0.3%
0.108 14
 
0.2%
0.106 13
 
0.2%
0.115 12
 
0.2%
0.122 12
 
0.2%
0.111 12
 
0.2%
0.117 11
 
0.2%
0.162 11
 
0.2%
Other values (4742) 5722
97.7%
ValueCountFrequency (%)
0.0116 1
< 0.1%
0.0222 1
< 0.1%
0.0224 1
< 0.1%
0.025 1
< 0.1%
0.0258 1
< 0.1%
0.0269 1
< 0.1%
0.0278 1
< 0.1%
0.0302 1
< 0.1%
0.03045 1
< 0.1%
0.0311 2
< 0.1%
ValueCountFrequency (%)
0.973 1
< 0.1%
0.972 1
< 0.1%
0.965 1
< 0.1%
0.964 1
< 0.1%
0.962 1
< 0.1%
0.96 2
< 0.1%
0.951 1
< 0.1%
0.95 1
< 0.1%
0.932 2
< 0.1%
0.922 1
< 0.1%

speechiness
Real number (ℝ)

Distinct4363
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.069695683
Minimum0.0232
Maximum0.958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2023-11-17T01:05:16.127634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0232
5-th percentile0.030580416
Q10.038141965
median0.048661667
Q30.07289
95-th percentile0.171935
Maximum0.958
Range0.9348
Interquartile range (IQR)0.034748036

Descriptive statistics

Standard deviation0.075353648
Coefficient of variation (CV)1.081181
Kurtosis66.380112
Mean0.069695683
Median Absolute Deviation (MAD)0.013258644
Skewness6.9468248
Sum407.99853
Variance0.0056781722
MonotonicityNot monotonic
2023-11-17T01:05:16.270015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0367 12
 
0.2%
0.0328 11
 
0.2%
0.0372 11
 
0.2%
0.0356 11
 
0.2%
0.0331 10
 
0.2%
0.0314 10
 
0.2%
0.0352 9
 
0.2%
0.0353 9
 
0.2%
0.0307 9
 
0.2%
0.0312 9
 
0.2%
Other values (4353) 5753
98.3%
ValueCountFrequency (%)
0.0232 1
 
< 0.1%
0.0236 1
 
< 0.1%
0.024 1
 
< 0.1%
0.0243 1
 
< 0.1%
0.0245 2
< 0.1%
0.0246 2
< 0.1%
0.0252 2
< 0.1%
0.0253 1
 
< 0.1%
0.0256 3
0.1%
0.0257 1
 
< 0.1%
ValueCountFrequency (%)
0.958 1
< 0.1%
0.95 1
< 0.1%
0.948 1
< 0.1%
0.945 2
< 0.1%
0.9416 1
< 0.1%
0.9396 1
< 0.1%
0.936666667 1
< 0.1%
0.924 1
< 0.1%
0.92 1
< 0.1%
0.913 1
< 0.1%

duration_ms
Real number (ℝ)

Distinct5745
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean246334.3
Minimum45707
Maximum1640000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2023-11-17T01:05:16.420019image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum45707
5-th percentile150900.48
Q1197228.25
median234992.78
Q3276223.93
95-th percentile384574.24
Maximum1640000
Range1594293
Interquartile range (IQR)78995.679

Descriptive statistics

Standard deviation83670.17
Coefficient of variation (CV)0.33966106
Kurtosis36.952699
Mean246334.3
Median Absolute Deviation (MAD)39086.309
Skewness3.7299188
Sum1.442041 × 109
Variance7.0006974 × 109
MonotonicityNot monotonic
2023-11-17T01:05:16.565697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
465713.5 3
 
0.1%
304491.34 3
 
0.1%
179800 3
 
0.1%
627330 3
 
0.1%
310919.1286 3
 
0.1%
181733 3
 
0.1%
197333 2
 
< 0.1%
183440 2
 
< 0.1%
427360 2
 
< 0.1%
265627 2
 
< 0.1%
Other values (5735) 5828
99.6%
ValueCountFrequency (%)
45707 1
< 0.1%
66333 1
< 0.1%
70587.61538 1
< 0.1%
71400 1
< 0.1%
74033.5 1
< 0.1%
74320 1
< 0.1%
79333 1
< 0.1%
85206.5 1
< 0.1%
87896.75 1
< 0.1%
88822 1
< 0.1%
ValueCountFrequency (%)
1640000 1
< 0.1%
1415707 1
< 0.1%
1260000 1
< 0.1%
1204090 1
< 0.1%
1187042 1
< 0.1%
1171360 1
< 0.1%
1087660 1
< 0.1%
902051.8788 1
< 0.1%
860706.8 1
< 0.1%
853670 1
< 0.1%

popularity
Real number (ℝ)

HIGH CORRELATION 

Distinct2652
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.595845
Minimum0
Maximum81
Zeros33
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2023-11-17T01:05:16.720048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14.333333
Q132.333333
median40.285714
Q348.5
95-th percentile59.5
Maximum81
Range81
Interquartile range (IQR)16.166667

Descriptive statistics

Standard deviation13.229546
Coefficient of variation (CV)0.3341145
Kurtosis0.52266759
Mean39.595845
Median Absolute Deviation (MAD)8.0754386
Skewness-0.50656529
Sum231794.08
Variance175.02089
MonotonicityNot monotonic
2023-11-17T01:05:16.862713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42 81
 
1.4%
40 81
 
1.4%
39 72
 
1.2%
43 71
 
1.2%
44 67
 
1.1%
46 67
 
1.1%
37 66
 
1.1%
41 63
 
1.1%
38 62
 
1.1%
35 62
 
1.1%
Other values (2642) 5162
88.2%
ValueCountFrequency (%)
0 33
0.6%
0.2 1
 
< 0.1%
0.301724138 1
 
< 0.1%
0.333333333 1
 
< 0.1%
0.384615385 1
 
< 0.1%
0.410958904 1
 
< 0.1%
0.538461538 1
 
< 0.1%
0.571428571 1
 
< 0.1%
0.6 1
 
< 0.1%
0.823529412 1
 
< 0.1%
ValueCountFrequency (%)
81 1
< 0.1%
77.50704225 1
< 0.1%
77.03846154 1
< 0.1%
77 1
< 0.1%
76.83333333 1
< 0.1%
76 1
< 0.1%
74.8 1
< 0.1%
74 2
< 0.1%
73.5 2
< 0.1%
73.24193548 1
< 0.1%

count
Real number (ℝ)

Distinct300
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.948241
Minimum1
Maximum1369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.9 KiB
2023-11-17T01:05:17.027985image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median10
Q331
95-th percentile140
Maximum1369
Range1368
Interquartile range (IQR)27

Descriptive statistics

Standard deviation77.829416
Coefficient of variation (CV)2.2925906
Kurtosis78.902769
Mean33.948241
Median Absolute Deviation (MAD)8
Skewness7.3327496
Sum198733
Variance6057.418
MonotonicityDecreasing
2023-11-17T01:05:17.164347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1100
18.8%
4 583
 
10.0%
6 388
 
6.6%
8 276
 
4.7%
10 240
 
4.1%
12 201
 
3.4%
14 160
 
2.7%
18 148
 
2.5%
16 148
 
2.5%
1 139
 
2.4%
Other values (290) 2471
42.2%
ValueCountFrequency (%)
1 139
 
2.4%
2 1100
18.8%
3 78
 
1.3%
4 583
10.0%
5 76
 
1.3%
6 388
 
6.6%
7 68
 
1.2%
8 276
 
4.7%
9 44
 
0.8%
10 240
 
4.1%
ValueCountFrequency (%)
1369 1
< 0.1%
1207 1
< 0.1%
1104 1
< 0.1%
1095 1
< 0.1%
1092 1
< 0.1%
1035 1
< 0.1%
994 1
< 0.1%
990 1
< 0.1%
938 1
< 0.1%
864 1
< 0.1%

Interactions

2023-11-17T01:05:09.196720image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:40.306450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:43.088178image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:44.895812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:46.747996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:48.526060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:50.438356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:54.122888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:56.737850image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:58.487885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:00.588614image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:02.365657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:04.117447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:05.968987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:09.493205image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:40.560447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:43.226546image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:45.029782image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:46.878292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:48.672910image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:50.569129image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:54.374590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:56.865657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:58.629386image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:00.729217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:02.499261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:04.250802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:06.187940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:10.120031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:40.746856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:43.351559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:45.151784image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:46.994095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:48.798311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:50.694017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:54.558037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:56.982697image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:58.754403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:00.845292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:02.630100image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:04.381133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:06.411077image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:10.240235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:40.982908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:43.475839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:45.269313image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:47.117244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:48.931462image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:50.816716image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:54.762015image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:57.106201image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:58.879467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:00.973306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:02.746934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:04.515310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:06.664618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:10.363049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:41.207051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:43.596392image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:45.411140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:47.247735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:49.059787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:50.944590image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:55.000868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:57.229388image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:59.005211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:01.096114image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:02.867203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:04.648125image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:06.916483image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:10.498970image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:41.456402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:43.729888image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:45.564229image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:47.388825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:49.201350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:52.230963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:55.181534image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:57.359699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:59.137060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:01.221864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:02.998397image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:04.785470image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:07.158991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:10.619537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:41.712866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:43.852827image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:45.684364image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:47.506993image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:49.337086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:52.356913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:55.387034image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:57.471735image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:59.254056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:01.338906image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:03.112821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:04.907375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:07.420949image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:10.751316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:41.936376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:43.981389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:45.812488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:47.624616image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:49.474475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:52.534240image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:55.660575image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:57.604731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:59.379173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:01.462214image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:03.239417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:05.030874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:07.636059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:10.882144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:42.203194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:44.106635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:45.935899image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:47.758398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:49.603712image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:52.716058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:55.933440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:57.724544image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:59.504429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:01.593050image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:03.359972image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:05.159653image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:07.781564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:11.012203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:42.391632image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:44.229312image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:46.065062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:47.877750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:49.739690image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:52.980461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:56.095618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:57.847703image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:59.641135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:01.723309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:03.486976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:05.290318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:08.007020image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:11.134825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:42.527679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:44.362938image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:46.194498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:47.997611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:49.869278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:53.199982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:56.220172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:57.968005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:59.762468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:01.842941image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:03.605811image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:05.422825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:08.266173image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:11.260208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:42.659413image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:44.492761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:46.329461image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:48.120252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:49.997675image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:53.448385image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:56.348566image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:58.088065image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:59.885866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:01.963861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:03.736293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:05.555558image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:08.503794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:11.407005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:42.797009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:44.629476image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:46.472472image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:48.247713image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:50.146192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:53.688257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:56.481629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:58.224588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:00.015569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:02.095617image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:03.865513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:05.706853image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:08.757279image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:11.554968image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:42.937526image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:44.768235image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:46.617422image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:48.386930image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:50.288262image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:53.875515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:56.615943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:04:58.354768image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:00.447266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:02.225739image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:03.997416image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:05.840170image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-11-17T01:05:08.996033image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-11-17T01:05:17.301029image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
artist_iddanceabilityenergyvalencetempoloudnesskeyacousticnessinstrumentalnesslivenessspeechinessduration_mspopularitycountmode
artist_id1.0000.0270.059-0.076-0.0030.131-0.019-0.071-0.054-0.0170.038-0.0280.198-0.0230.048
danceability0.0271.0000.0070.564-0.1230.0730.012-0.026-0.188-0.1840.088-0.0140.170-0.0420.147
energy0.0590.0071.0000.1930.3310.7840.046-0.793-0.1070.1980.2770.0170.355-0.1070.055
valence-0.0760.5640.1931.0000.0940.0730.011-0.061-0.157-0.0080.057-0.220-0.128-0.0210.067
tempo-0.003-0.1230.3310.0941.0000.2500.016-0.291-0.0450.0660.100-0.0610.103-0.0000.056
loudness0.1310.0730.7840.0730.2501.0000.035-0.615-0.3100.1290.204-0.0680.521-0.0910.086
key-0.0190.0120.0460.0110.0160.0351.000-0.061-0.018-0.0080.019-0.0080.033-0.0370.151
acousticness-0.071-0.026-0.793-0.061-0.291-0.615-0.0611.0000.075-0.083-0.179-0.121-0.3960.1440.080
instrumentalness-0.054-0.188-0.107-0.157-0.045-0.310-0.0180.0751.000-0.018-0.0260.244-0.2370.2020.089
liveness-0.017-0.1840.198-0.0080.0660.129-0.008-0.083-0.0181.0000.178-0.069-0.0810.2120.095
speechiness0.0380.0880.2770.0570.1000.2040.019-0.179-0.0260.1781.000-0.0320.0260.0820.071
duration_ms-0.028-0.0140.017-0.220-0.061-0.068-0.008-0.1210.244-0.069-0.0321.0000.125-0.0080.122
popularity0.1980.1700.355-0.1280.1030.5210.033-0.396-0.237-0.0810.0260.1251.000-0.1380.109
count-0.023-0.042-0.107-0.021-0.000-0.091-0.0370.1440.2020.2120.082-0.008-0.1381.0000.067
mode0.0480.1470.0550.0670.0560.0860.1510.0800.0890.0950.0710.1220.1090.0671.000

Missing values

2023-11-17T01:05:11.753115image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-17T01:05:11.997658image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

artist_nameartist_iddanceabilityenergyvalencetempoloudnessmodekeyacousticnessinstrumentalnesslivenessspeechinessduration_mspopularitycount
0Frank Sinatra7925070.3844780.2380170.364288110.181698-14.271141150.7356480.0208550.2321060.049614189179.925526.0043831369
1Vladimir Horowitz1191070.3432100.1188440.22595194.900679-23.193418110.9900700.8795080.1838120.043360266541.12513.5923781207
2Johnny Cash8168900.6198030.4493810.680662115.037747-11.5931041100.6856370.0226470.2422430.098216162279.267226.6141301104
3Billie Holiday790160.5726370.2013680.498934109.912172-13.225966150.9084990.0130640.2177270.062432185131.453015.6210051095
4Bob Dylan669150.5125980.4779320.551934126.160149-11.184330170.5625670.0342110.3089780.064535256713.420330.8608061092
5The Rolling Stones8944650.5244460.7199150.655332123.764717-7.830265100.2937880.1761370.2684430.051440229705.962334.5739131035
6The Beach Boys418740.5029450.5321310.633957125.992036-9.925742190.3984880.1153630.1911890.043971148845.094627.957746994
7Elvis Presley1802280.4958430.4263080.621249111.489453-12.893730100.7414120.0536230.2473460.058278156211.035433.391919990
8Wolfgang Amadeus Mozart263500.3533460.1378690.330529108.604340-20.174257170.9615720.5088810.1888280.068485329702.92438.936034938
9Miles Davis4238290.4602210.3082290.417860113.550382-14.526619000.6557110.2050730.2197620.054571404023.445622.700231864
artist_nameartist_iddanceabilityenergyvalencetempoloudnessmodekeyacousticnessinstrumentalnesslivenessspeechinessduration_mspopularitycount
5844Jonn Hart30437870.6860.5410.23796.965-6.643040.0971000.0000000.42300.0356189613.049.01
5845Hiatus Kaiyote30456200.5520.5310.32491.573-6.767180.6560000.0000520.11000.1250275320.057.01
5846Gawvi30995730.6830.6300.452159.927-5.146110.3200000.0000000.22200.0587203325.060.01
5847Adrian Marcel31204580.8490.5340.599102.014-6.365140.0472000.0000000.25000.0699237653.059.01
5848Karen Harding33086650.7010.6670.675119.975-6.177170.0121000.0000070.14800.0361213135.067.01
5849Natalie La Rose33595190.8300.5200.735104.990-8.714100.0007920.0000130.06560.0376189907.064.01
5850Sarah Ross33815660.7210.9440.62685.002-5.982180.0130000.0000000.32000.1590262760.052.01
5851Rotimi34102500.6370.5010.431103.993-6.148000.2290000.0000590.09900.1870185461.071.01
5852Jillian Jacqueline34559450.5470.6720.283155.791-5.0231110.3040000.0000000.09960.0496213133.058.01
5853Jaira Burns36396180.5660.7690.385170.036-4.342170.0183000.0000000.10800.0872191100.074.01